This is the implementation of our AAAI 2020 paper NeoNav: Improving the Generalization of Visual Navigation via Generating Next Expected Observations, training and evaluation on Active Vision Dataset (depth only).
- The environment: Cuda 10.0, Python 3.6.4, PyTorch 1.0.1
- Please download "depth_imgs.npy" file from the AVD_Minimal and put the file in the train folder.
- Please download our training data HERE.
- Our trained model can be downloaded from HERE. If you plan to train your own navigation model from scratch, some suggestions are provided:
- Pre-train the model by using "python3 ttrain.py" and terminate the training when the action prediction accuracy approaches 70%.
- Use "python3 train.py" to train the NeoNav model.
- To evaluate our model, please run "python3 ./test/evaluate.py" or "python3 ./test/evaluete_with_stop.py"
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To ask questions or report issues please open an issue on the issues tracker.
If you use NeoNav in your research, please cite the paper:
@article{wuneonav,
title={NeoNav: Improving the Generalization of Visual Navigation via Generating Next Expected Observations},
author={Wu, Qiaoyun and Manocha, Dinesh and Wang, Jun and Xu, Kai}
}